Deep Semisupervised Class- and Correlation-Collapsed Cross-View Learning

Deep Semisupervised Class- and Correlation-Collapsed Cross-View Learning
Title:
Deep Semisupervised Class- and Correlation-Collapsed Cross-View Learning
Other Titles:
IEEE Transactions on Cybernetics
Publication Date:
07 May 2021
Citation:
X. Wang, P. Hu, P. Liu, and D. Peng, “Deep semisupervisedclass-and correlation-collapsed cross-view learning,”IEEE Transactionson Cybernetics, to be published. [Online]. Available: https://ieeexplore.ieee.org/document/8994196
Abstract:
In many computer vision applications, an object can be represented by multiple different views. Due to the heterogeneous gap triggered by the different views' inconsistent distributions, it is challenging to exploit these multiview data for cross-view retrieval and classification. Motivated by the fact that both labeled and unlabeled data can enhance the relations among different views, this article proposes a deep cross-view learning framework called deep semisupervised classes- and correlation-collapsed cross-view learning (DSC³L) for cross-view retrieval and classification. Different from the existing methods which focus on the two-view problems, the proposed method learns U (generally U≥2) view-specific deep transformations to gradually project U different views into a shared space in which the projection embraces the supervised learning and the unsupervised learning. We propose collapsing the instances of the same class from all views into the same point, with the instances of different classes into distinct points simultaneously. Second, to exploit the abundant unlabeled U-wise multiview data, we propose to collapse-correlated data into the same point, with the uncorrelated data into distinct points. Specifically, these two processes are formulated to minimize the two Kullback-Leibler (KL) divergences between the conditional distribution and a desirable one, for each instance. Finally, the two KL divergences are integrated into a joint optimization to learn a discriminative shared space. The experimental results on five widely used public datasets demonstrate the effectiveness of the proposed method.
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Funding Info:
There is no specific funding for this research
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ISSN:
2168-2267
2168-2275
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